Overview

Dataset statistics

Number of variables20
Number of observations21597
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory3.3 MiB
Average record size in memory160.0 B

Variable types

Numeric17
Categorical3

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
price is highly correlated with bathrooms and 5 other fieldsHigh correlation
bedrooms is highly correlated with sqft_livingHigh correlation
bathrooms is highly correlated with price and 6 other fieldsHigh correlation
sqft_living is highly correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
floors is highly correlated with yr_builtHigh correlation
grade is highly correlated with price and 5 other fieldsHigh correlation
sqft_above is highly correlated with price and 5 other fieldsHigh correlation
yr_built is highly correlated with bathrooms and 5 other fieldsHigh correlation
zipcode is highly correlated with yr_built and 2 other fieldsHigh correlation
long is highly correlated with yr_built and 1 other fieldsHigh correlation
sqft_living15 is highly correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
waterfront is highly correlated with viewHigh correlation
view is highly correlated with waterfrontHigh correlation
condition is highly correlated with yr_builtHigh correlation
sqft_basement is highly correlated with price and 3 other fieldsHigh correlation
lat is highly correlated with zipcodeHigh correlation
sqft_basement has 13110 (60.7%) zeros Zeros
yr_renovated has 20683 (95.8%) zeros Zeros

Reproduction

Analysis started2022-11-16 11:51:38.678181
Analysis finished2022-11-16 11:53:03.349888
Duration1 minute and 24.67 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

Distinct21420
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4580474288
Minimum1000102
Maximum9900000190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:03.501817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile512740390
Q12123049175
median3904930410
Q37308900490
95-th percentile9297300412
Maximum9900000190
Range9899000088
Interquartile range (IQR)5185851315

Descriptive statistics

Standard deviation2876735716
Coefficient of variation (CV)0.6280431971
Kurtosis-1.260749894
Mean4580474288
Median Absolute Deviation (MAD)2402530270
Skewness0.243225522
Sum9.892450319 × 1013
Variance8.275608378 × 1018
MonotonicityNot monotonic
2022-11-16T17:23:03.703930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7950006203
 
< 0.1%
63080000102
 
< 0.1%
35280000402
 
< 0.1%
33030001302
 
< 0.1%
86515103802
 
< 0.1%
66690202902
 
< 0.1%
7260491902
 
< 0.1%
79615000102
 
< 0.1%
20192002202
 
< 0.1%
44350007052
 
< 0.1%
Other values (21410)21576
99.9%
ValueCountFrequency (%)
10001022
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
36000721
< 0.1%
38000081
< 0.1%
52000871
< 0.1%
62000171
< 0.1%
72000801
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000951
< 0.1%
98423000361
< 0.1%
98393011651
< 0.1%
98393010601
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3622
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540296.5735
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:03.873620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1322000
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7622000
Interquartile range (IQR)323000

Descriptive statistics

Standard deviation367368.1401
Coefficient of variation (CV)0.6799379417
Kurtosis34.54135858
Mean540296.5735
Median Absolute Deviation (MAD)150000
Skewness4.023364652
Sum1.16687851 × 1010
Variance1.349593504 × 1011
MonotonicityNot monotonic
2022-11-16T17:23:04.019178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000172
 
0.8%
450000172
 
0.8%
550000159
 
0.7%
500000152
 
0.7%
425000150
 
0.7%
325000148
 
0.7%
400000145
 
0.7%
375000138
 
0.6%
300000133
 
0.6%
525000131
 
0.6%
Other values (3612)20097
93.1%
ValueCountFrequency (%)
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
865001
< 0.1%
890001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70600001
< 0.1%
68900001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51100001
< 0.1%
46700001
< 0.1%
45000001
< 0.1%
44900001
< 0.1%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.373199981
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:04.149893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9262988945
Coefficient of variation (CV)0.2746053894
Kurtosis49.82183475
Mean3.373199981
Median Absolute Deviation (MAD)1
Skewness2.023641235
Sum72851
Variance0.858029642
MonotonicityNot monotonic
2022-11-16T17:23:04.264357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
39824
45.5%
46882
31.9%
22760
 
12.8%
51601
 
7.4%
6272
 
1.3%
1196
 
0.9%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
103
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
1196
 
0.9%
22760
 
12.8%
39824
45.5%
46882
31.9%
51601
 
7.4%
6272
 
1.3%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
813
 
0.1%
738
 
0.2%
6272
 
1.3%
51601
 
7.4%
46882
31.9%
39824
45.5%

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.115826272
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:04.488637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7689842967
Coefficient of variation (CV)0.3634439683
Kurtosis1.279315294
Mean2.115826272
Median Absolute Deviation (MAD)0.5
Skewness0.5197092816
Sum45695.5
Variance0.5913368485
MonotonicityNot monotonic
2022-11-16T17:23:04.602166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.55377
24.9%
13851
17.8%
1.753048
14.1%
2.252047
 
9.5%
21930
 
8.9%
1.51445
 
6.7%
2.751185
 
5.5%
3753
 
3.5%
3.5731
 
3.4%
3.25589
 
2.7%
Other values (19)641
 
3.0%
ValueCountFrequency (%)
0.54
 
< 0.1%
0.7571
 
0.3%
13851
17.8%
1.259
 
< 0.1%
1.51445
 
6.7%
1.753048
14.1%
21930
 
8.9%
2.252047
 
9.5%
2.55377
24.9%
2.751185
 
5.5%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
66
< 0.1%
5.754
 
< 0.1%
5.510
< 0.1%
5.2513
0.1%

sqft_living
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1034
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2080.32185
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:04.749111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3760
Maximum13540
Range13170
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.1061251
Coefficient of variation (CV)0.4413288862
Kurtosis5.252101951
Mean2080.32185
Median Absolute Deviation (MAD)540
Skewness1.473215455
Sum44928711
Variance842918.8569
MonotonicityNot monotonic
2022-11-16T17:23:04.898657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300138
 
0.6%
1400135
 
0.6%
1440133
 
0.6%
1660129
 
0.6%
1800129
 
0.6%
1010129
 
0.6%
1820128
 
0.6%
1480125
 
0.6%
1720125
 
0.6%
1560124
 
0.6%
Other values (1024)20302
94.0%
ValueCountFrequency (%)
3701
< 0.1%
3801
< 0.1%
3901
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
4702
< 0.1%
4802
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

sqft_lot
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9776
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099.40876
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:05.078052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800.8
Q15040
median7618
Q310685
95-th percentile43307.2
Maximum1651359
Range1650839
Interquartile range (IQR)5645

Descriptive statistics

Standard deviation41412.63688
Coefficient of variation (CV)2.742666122
Kurtosis285.4958119
Mean15099.40876
Median Absolute Deviation (MAD)2618
Skewness13.07260357
Sum326101931
Variance1715006493
MonotonicityNot monotonic
2022-11-16T17:23:05.236066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000358
 
1.7%
6000290
 
1.3%
4000251
 
1.2%
7200220
 
1.0%
7500119
 
0.6%
4800119
 
0.6%
4500114
 
0.5%
8400111
 
0.5%
9600109
 
0.5%
3600103
 
0.5%
Other values (9766)19803
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%

floors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494096402
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:05.375308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.539682791
Coefficient of variation (CV)0.3612101536
Kurtosis-0.4910657592
Mean1.494096402
Median Absolute Deviation (MAD)0.5
Skewness0.6144969756
Sum32268
Variance0.2912575149
MonotonicityNot monotonic
2022-11-16T17:23:05.467900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110673
49.4%
28235
38.1%
1.51910
 
8.8%
3611
 
2.8%
2.5161
 
0.7%
3.57
 
< 0.1%
ValueCountFrequency (%)
110673
49.4%
1.51910
 
8.8%
28235
38.1%
2.5161
 
0.7%
3611
 
2.8%
3.57
 
< 0.1%
ValueCountFrequency (%)
3.57
 
< 0.1%
3611
 
2.8%
2.5161
 
0.7%
28235
38.1%
1.51910
 
8.8%
110673
49.4%

waterfront
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
0
21434 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

Length

2022-11-16T17:23:05.572363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-16T17:23:05.750600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021434
99.2%
1163
 
0.8%

view
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
0
19475 
2
 
961
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Length

2022-11-16T17:23:05.886431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-16T17:23:06.037990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019475
90.2%
2961
 
4.4%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
3
14020 
4
5677 
5
1701 
2
 
170
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

Length

2022-11-16T17:23:06.178365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-16T17:23:06.315190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

Most occurring characters

ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314020
64.9%
45677
26.3%
51701
 
7.9%
2170
 
0.8%
129
 
0.1%

grade
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.657915451
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:06.513216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.173199664
Coefficient of variation (CV)0.1532009163
Kurtosis1.135148022
Mean7.657915451
Median Absolute Deviation (MAD)1
Skewness0.7882366364
Sum165388
Variance1.376397451
MonotonicityNot monotonic
2022-11-16T17:23:06.602106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
78974
41.6%
86065
28.1%
92615
 
12.1%
62038
 
9.4%
101134
 
5.3%
11399
 
1.8%
5242
 
1.1%
1289
 
0.4%
427
 
0.1%
1313
 
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
427
 
0.1%
5242
 
1.1%
62038
 
9.4%
78974
41.6%
86065
28.1%
92615
 
12.1%
101134
 
5.3%
11399
 
1.8%
1289
 
0.4%
ValueCountFrequency (%)
1313
 
0.1%
1289
 
0.4%
11399
 
1.8%
101134
 
5.3%
92615
 
12.1%
86065
28.1%
78974
41.6%
62038
 
9.4%
5242
 
1.1%
427
 
0.1%

sqft_above
Real number (ℝ≥0)

HIGH CORRELATION

Distinct942
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.596842
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:06.734916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation827.7597612
Coefficient of variation (CV)0.4627984024
Kurtosis3.405519761
Mean1788.596842
Median Absolute Deviation (MAD)450
Skewness1.447434235
Sum38628326
Variance685186.2222
MonotonicityNot monotonic
2022-11-16T17:23:06.876695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300212
 
1.0%
1010210
 
1.0%
1200206
 
1.0%
1220192
 
0.9%
1140184
 
0.9%
1400180
 
0.8%
1060178
 
0.8%
1180177
 
0.8%
1340176
 
0.8%
1250174
 
0.8%
Other values (932)19708
91.3%
ValueCountFrequency (%)
3701
 
< 0.1%
3801
 
< 0.1%
3901
 
< 0.1%
4101
 
< 0.1%
4202
< 0.1%
4301
 
< 0.1%
4401
 
< 0.1%
4601
 
< 0.1%
4702
< 0.1%
4804
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
67201
< 0.1%

sqft_basement
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.7250081
Minimum0
Maximum4820
Zeros13110
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:07.013638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.6678004
Coefficient of variation (CV)1.517414648
Kurtosis2.711798303
Mean291.7250081
Median Absolute Deviation (MAD)0
Skewness1.576889627
Sum6300385
Variance195954.7815
MonotonicityNot monotonic
2022-11-16T17:23:07.145715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013110
60.7%
600221
 
1.0%
700218
 
1.0%
500214
 
1.0%
800206
 
1.0%
400184
 
0.9%
1000149
 
0.7%
900144
 
0.7%
300142
 
0.7%
200108
 
0.5%
Other values (296)6901
32.0%
ValueCountFrequency (%)
013110
60.7%
102
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
5011
 
0.1%
6010
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8020
 
0.1%
9021
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
30001
< 0.1%
28501
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%

yr_built
Real number (ℝ≥0)

HIGH CORRELATION

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.999676
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:07.281838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.37523413
Coefficient of variation (CV)0.01490372347
Kurtosis-0.6576944258
Mean1970.999676
Median Absolute Deviation (MAD)23
Skewness-0.4694499765
Sum42567680
Variance862.9043803
MonotonicityNot monotonic
2022-11-16T17:23:07.422157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014559
 
2.6%
2006453
 
2.1%
2005450
 
2.1%
2004433
 
2.0%
2003420
 
1.9%
1977417
 
1.9%
2007417
 
1.9%
1978387
 
1.8%
1968381
 
1.8%
2008367
 
1.7%
Other values (106)17313
80.2%
ValueCountFrequency (%)
190087
0.4%
190129
 
0.1%
190227
 
0.1%
190346
0.2%
190445
0.2%
190574
0.3%
190692
0.4%
190765
0.3%
190886
0.4%
190994
0.4%
ValueCountFrequency (%)
201538
 
0.2%
2014559
2.6%
2013201
 
0.9%
2012170
 
0.8%
2011130
 
0.6%
2010143
 
0.7%
2009230
1.1%
2008367
1.7%
2007417
1.9%
2006453
2.1%

yr_renovated
Real number (ℝ≥0)

ZEROS

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.46478678
Minimum0
Maximum2015
Zeros20683
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:07.608027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.8214379
Coefficient of variation (CV)4.757265759
Kurtosis18.68367598
Mean84.46478678
Median Absolute Deviation (MAD)0
Skewness4.547572443
Sum1824186
Variance161460.468
MonotonicityNot monotonic
2022-11-16T17:23:07.775389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020683
95.8%
201491
 
0.4%
201337
 
0.2%
200336
 
0.2%
200035
 
0.2%
200535
 
0.2%
200735
 
0.2%
200426
 
0.1%
199025
 
0.1%
200624
 
0.1%
Other values (60)570
 
2.6%
ValueCountFrequency (%)
020683
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
ValueCountFrequency (%)
201516
 
0.1%
201491
0.4%
201337
0.2%
201211
 
0.1%
201113
 
0.1%
201018
 
0.1%
200922
 
0.1%
200818
 
0.1%
200735
 
0.2%
200624
 
0.1%

zipcode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.95185
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:07.926521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.51307235
Coefficient of variation (CV)0.0005456177596
Kurtosis-0.8540048606
Mean98077.95185
Median Absolute Deviation (MAD)42
Skewness0.4053221913
Sum2118189526
Variance2863.648913
MonotonicityNot monotonic
2022-11-16T17:23:08.084175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103602
 
2.8%
98038589
 
2.7%
98115583
 
2.7%
98052574
 
2.7%
98117553
 
2.6%
98042547
 
2.5%
98034545
 
2.5%
98118507
 
2.3%
98023499
 
2.3%
98006498
 
2.3%
Other values (60)16100
74.5%
ValueCountFrequency (%)
98001361
1.7%
98002199
 
0.9%
98003280
1.3%
98004317
1.5%
98005168
 
0.8%
98006498
2.3%
98007141
 
0.7%
98008283
1.3%
98010100
 
0.5%
98011195
 
0.9%
ValueCountFrequency (%)
98199317
1.5%
98198280
1.3%
98188136
 
0.6%
98178262
1.2%
98177255
1.2%
98168269
1.2%
98166254
1.2%
98155446
2.1%
9814857
 
0.3%
98146288
1.3%

lat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5033
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56009299
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:08.248094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.5718
Q347.678
95-th percentile47.7497
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.1385517682
Coefficient of variation (CV)0.002913193803
Kurtosis-0.6757902106
Mean47.56009299
Median Absolute Deviation (MAD)0.1049
Skewness-0.48552159
Sum1027155.328
Variance0.01919659246
MonotonicityNot monotonic
2022-11-16T17:23:08.408059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.684617
 
0.1%
47.549117
 
0.1%
47.662417
 
0.1%
47.532217
 
0.1%
47.688616
 
0.1%
47.671116
 
0.1%
47.695516
 
0.1%
47.684215
 
0.1%
47.68615
 
0.1%
47.540215
 
0.1%
Other values (5023)21436
99.3%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18031
< 0.1%
47.18081
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
 
< 0.1%
47.7772
 
< 0.1%
47.77693
< 0.1%
47.77682
 
< 0.1%
47.77676
< 0.1%
47.77664
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION

Distinct751
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139825
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21597
Negative (%)100.0%
Memory size168.9 KiB
2022-11-16T17:23:08.622883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.231
Q3-122.125
95-th percentile-121.9798
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.1407235288
Coefficient of variation (CV)-0.001151451953
Kurtosis1.052120317
Mean-122.2139825
Median Absolute Deviation (MAD)0.101
Skewness0.8848883395
Sum-2639455.38
Variance0.01980311157
MonotonicityNot monotonic
2022-11-16T17:23:08.783649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29115
 
0.5%
-122.3111
 
0.5%
-122.362104
 
0.5%
-122.291100
 
0.5%
-122.36399
 
0.5%
-122.37299
 
0.5%
-122.28898
 
0.5%
-122.35796
 
0.4%
-122.28495
 
0.4%
-122.17294
 
0.4%
Other values (741)20586
95.3%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5151
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5071
 
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3642
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%

sqft_living15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.620318
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:08.951916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.2304719
Coefficient of variation (CV)0.3449227141
Kurtosis1.591732789
Mean1986.620318
Median Absolute Deviation (MAD)410
Skewness1.106875397
Sum42905039
Variance469540.7996
MonotonicityNot monotonic
2022-11-16T17:23:09.109154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540197
 
0.9%
1440195
 
0.9%
1560192
 
0.9%
1500180
 
0.8%
1460169
 
0.8%
1580167
 
0.8%
1800166
 
0.8%
1610166
 
0.8%
1720166
 
0.8%
1620164
 
0.8%
Other values (767)19835
91.8%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7102
 
< 0.1%
7202
 
< 0.1%
7408
< 0.1%
7503
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57906
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%

sqft_lot15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8682
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12758.28351
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2022-11-16T17:23:09.281759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2002.4
Q15100
median7620
Q310083
95-th percentile37045.2
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27274.44195
Coefficient of variation (CV)2.137783027
Kurtosis151.3956625
Mean12758.28351
Median Absolute Deviation (MAD)2505
Skewness9.524361965
Sum275540649
Variance743895183.7
MonotonicityNot monotonic
2022-11-16T17:23:09.488474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000427
 
2.0%
4000356
 
1.6%
6000288
 
1.3%
7200210
 
1.0%
4800145
 
0.7%
7500142
 
0.7%
8400116
 
0.5%
3600111
 
0.5%
4500111
 
0.5%
5100109
 
0.5%
Other values (8672)19582
90.7%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
7941
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3920402
< 0.1%
3868121
< 0.1%

Interactions

2022-11-16T17:22:58.066758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:21:57.024492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:01.106446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:05.126460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:08.557733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:11.836079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:15.550329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:18.357225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:21.034613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:38.654279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:40.851404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:43.315449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:45.677706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:48.587858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:51.022823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:53.415955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:55.810642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:58.233433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:21:57.326462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:01.417558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:05.324425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-11-16T17:21:59.952337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:04.104387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:07.912239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:11.189798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:14.577308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:17.650730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:20.360093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:38.082435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:40.336798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:42.726864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:45.136759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:47.975263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:50.468235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:52.809200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:55.309445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:57.577073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:23:00.592816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:00.412775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:04.335437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:08.060103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:11.335268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:14.736562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:17.832478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:20.519043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:38.273510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:40.465615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:42.852052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:45.298074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:48.134430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:50.602368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:52.975949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:55.434308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:57.698838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:23:00.713499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:00.742297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:04.566223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:08.224345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:11.512226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:14.931548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:17.986681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:20.673256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:38.402326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:40.590257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:42.978791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:45.418370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:48.305920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:50.768227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:53.122178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:55.570016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:57.825605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:23:00.838380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:00.937719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:04.726265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:08.417319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:11.702652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:15.126254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:18.165055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:20.843617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:38.537346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:40.721734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:43.195828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:45.541558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:48.430663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:50.900233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:53.266279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:55.693988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-16T17:22:57.950696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-16T17:23:09.863455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-16T17:23:10.156696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-16T17:23:10.434948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-16T17:23:10.861414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-16T17:23:11.237392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-16T17:23:11.395794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-16T17:23:01.130543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-16T17:23:01.596019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
091060000051310000.042.25375050002.0005824401310192409811547.6747-122.30321704590
15101400871445500.021.75139066701.00036720670194109811547.6914-122.3089206380
27923600250450000.052.00187073441.5003718700196009800747.5951-122.14418707650
38730000270359000.022.75137011402.000381080290200909813347.7052-122.34313701090
491786016601700000.053.00332053542.0003933200200409810347.6542-122.33123304040
51786200010456500.042.502580117802.0003925800200309803847.3658-122.04024108403
61422700040183000.031.00117073201.0003711700196209818847.4685-122.28220407320
76752600320360000.042.50202072892.0003720200199409803147.4010-122.17120907259
84166600610335000.032.001410448661.0004714100198509802347.3273-122.370295029152
97129304540440000.052.00143056001.5003614300194709811847.5192-122.26618605980

Last rows

idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
215872201501015430000.041.501920100001.000471070850195409800647.5725-122.133145010836
215882423600100491500.041.7521901254521.0023921900196809809247.2703-122.0693000125017
215897853220390785000.053.253660119952.00231036600200609806547.5337-121.860332011241
215909267200226436110.032.50177012353.000381600170200709810347.6965-122.34216801203
215912771102144385000.033.25132013272.000381040280200809819947.6506-122.38314401263
215923438501320295000.022.50163013682.000371280350200909810647.5489-122.36315902306
215937853361370555000.042.50331065002.0003833100201209806547.5150-121.87023805000
215948564860280459990.032.50268055392.0003826800201309804547.4761-121.73429906037
21595123059127625000.043.252730540141.0003915601170200709805947.5133-122.1102730111274
215963832050580300000.032.50254050502.0003725400200609804247.3358-122.05522805050

Duplicate rows

Most frequently occurring

idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15# duplicates
01825069031550000.041.75241084472.003482060350193619809807447.6499-122.0882520147892
16308000010585000.032.50229050892.0003922900200109800647.5443-122.172229079842
28648900110555000.032.50194032112.0003819400200909802747.5644-122.093188030782